In recent years, for rapidly developing mobile communication systems (for example, Personal Handy-phone Systems; referred to as PHS, hereinafter), there has been proposed a system which, in communication between a radio base device (base station) and mobile terminal devices (terminals), extracts received signals from a desired user's terminal by adaptive array processing, particularly at the base station.
The adaptive array process is a process in which based on signals received from terminals, a weight vector consisting of reception factors (weights) for the respective antennas in the base station is estimated and adaptive control is performed, so as to accurately extract a signal from a particular terminal.
There is provided at the base station a reception weight vector calculating unit for estimating such a weight vector for every symbol of received signals. The reception weight vector calculating unit executes a process for converging the weight vector to reduce the square of the error between a known reference signal and the sum of the results of the complex multiplication of received signals by the estimated weight vector, namely the adaptive array process for converging the reception directivity from a particular terminal.
In the adaptive array process, such convergence of the weight vector is performed in an adaptive manner in accordance with time or fluctuation of propagation path characteristics of signal radio waves, to remove interfering components or noise from the received signals to extract signals received from a particular terminal. Such an adaptive array process is expected to realize effective frequency utilization, transmission electric power reduction, improved communication quality, etc.
The reception weight vector calculating unit uses a sequential estimation algorithm such as RLS (Recursive Least Squares) algorithm, or LMS (Least Mean Square) algorithm, as the weight estimation algorithm.
Such RLS algorithm and LMS algorithm are well-known techniques in the field of adaptive array processing, and described in detail, for example, in “Chapter 3: MMSE Adaptive Array” in pp. 35–49 of “Adaptive Signal Processing by Array Antenna” by Nobuyoshi Kikuma (Kagaku Gijutsu Shuppan, Nov. 25, 1998). Therefore, description thereof will be omitted here.
Further, for mobile communication systems such as PHSs, in order to enhance radio wave frequency utilization efficiency, there has been proposed a PDMA (Path Division Multiple Access) system in which a single time slot is frequency-divided and a single frequency of a single time slot can be spatially divided to allow multiple users' terminals to access in multiple to a base station. In such a PDMA system, currently, the aforementioned adaptive array technique is used for realizing spatial multiplex accesses.
By the adaptive array proceeding as aforementioned, up-link signals from the respective antennas of a plurality of multiplexed users' terminals are received by the array antenna of a base station and separately extracted with reception directivity.
According to the PHS standard, four slots for realizing up-link communication and four slots for realizing down-link communication are provided in each frame.
A sequential estimation algorithm such as RLS or LMS employed in the aforementioned adaptive array processing requires setting of various types of parameters (hereinafter, referred to as array parameters) such as initial values or updating steps. The weight estimating ability of the sequential estimation algorithm varies depending on the set values of these array parameters.
More specifically, RLS algorithm requires two initial values, i.e., a weight initial value and a correlation initial value, and one updating step. LMS algorithm requires one weight initial value and one updating step.
The propagation environment of up-link signal radio waves from users' terminals diversely varies. Possible factors of such variations include various types of factors such as the degree of multiplexing of spatial multiple connections to the spatial multiplex base station, ratio of desired user's power to desired user's power (hereinafter, referred to as DD ratio), correlation values of received signals from multiplexed users, the amount of fading of multiplexed users' terminals, reception levels from multiplexed users' terminals, etc.
In conventional radio receiving devices using the adaptive array processing (e.g., spatial multiple base stations), various types of array parameters for the sequential estimation algorithm are fixed at predetermined values even if the propagation environment varies because of such various types of factors.
For example, in an adjustment stage before factory-shipment, the various types of array parameters of a radio receiving device are preset to predetermined values assuming harsh propagation environment (e.g., large fading condition) in order to realize favorable reception characteristics in such harsh propagation environment.
Since the various types of array parameters of a radio receiving device are preset to fixed predetermined values, there has been a problem that in a certain propagation environment the weight estimating ability is optimized with the predetermined array parameter values enabling optimal reception from a desired user terminal, but in different propagation environments the weight estimating ability is degraded with the array parameters, preventing optimal reception, which results in reception errors.
Therefore, it is an object of the present invention to provide radio receiving devices, array parameter optimal value estimation methods and array parameter optimal value estimation programs which estimate optimal values of array parameters appropriate to the propagation environment of received signals and change the array parameters in an adaptive manner to optimize the weight estimating ability regardless of variations of the propagation environment, thereby realizing optimal signal reception.